Systems and methods for dispatching and navigating an unmanned aerial vehicle

ABSTRACT

A system for dispatching and navigating an unmanned aerial vehicle (UAV) to a target location comprises a UAV and a navigation module comprising a processor and a memory storing a 3D map comprising the target location and machine-readable instructions such that, when executed by the navigation module processor, cause the processor to perform a method comprising identifying a location of the UAV with respect to the 3D map, receiving a target location input, identifying the target location with respect to the 3D map, generating at least one potential route connecting the location of the UAV and the target location, assigning to at least one potential route an evaluation score according to at least one route assessment criterion, selecting the potential route having the highest evaluation score as a preferred route, and transmitting the preferred route to the UAV.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of U.S. Provisional PatentApplication Ser. No. 63/127,469, filed on Dec. 18, 2020, which is hereinincorporated by reference in its entirety.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference in their entirety, as if eachindividual publication or patent application was specifically andindividually indicated to be incorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to the field of navigation and controlsystems, and more specifically to the field of autonomous navigation ofunmanned aerial vehicles (UAVs). Described herein are systems andmethods for dispatching and navigating a UAV.

BACKGROUND

Unmanned aerial vehicles (UAVs), commonly known as “drones,” have thepotential to be a powerful tool for disaster and emergency responseteams. When fitted with a camera or other sensors, UAVs offer arelatively affordable and expedient means to acquire informationregarding an ongoing disaster or emergency without endangering a humanactor or more expensive equipment. However, the difficulty of deployingand navigating a UAV prevent their widespread adoption for this role.

Flying a UAV is no simple task. It can require hours of training inorder to properly educate a professional to handle a UAVs' highmaneuverability but also vulnerability to wind and weather conditions.Furthermore, in many locales, especially in residential areas, variouslegislation prohibits the entry of UAVs into certain airspaces foreither safety (e.g., airports) or privacy concerns. A pilot of a UAVmust therefore be additionally versed in which areas he or she maynavigate the drone. Finally, during an emergency response operation thatseeks to employ a manually operated UAV, one member of the team mustremain fully committed to piloting the drone during its flight time. Incertain jurisdictions, such as rural areas, the response team may nothave the personnel to spare to that narrow of an activity.

Pre-existing automated UAVs suffer from similar problems. Some UAVs arecapable of maintaining a constant altitude in gentle weather conditionsbut will struggle under intense weather conditions or in areas where theterrain exhibits rapid changes in elevation or sudden, sharp obstacles,such as very hilly or mountainous regions or those that feature isolatedbut large groups of trees. UAVs of these types have an immense risk ofcrashing into stationary objects, like the aforementioned hillsides andtrees. Outfitting a drone with complicated digital vision systems toavoid these hazards dramatically increases the cost of the UAV, thusdiscouraging response teams to take the adequate risks with the UAV thatmay be necessary during an emergency response situation due to the fearof damaging or destroying the drone.

Therefore, there is a need for a new, useful, and cost-effective systemfor dispatching and navigating a UAV that overcomes at least theseabove-described limitations.

SUMMARY

One aspect of the disclosure herein includes for, in some embodiments, asystem for dispatching and navigating an unmanned aerial vehicle (UAV)to a target location comprising: a UAV; and a navigation module incommunication with the UAV, the navigation module comprising: anavigation module processor; and a navigation module memory storing a 3Dmap comprising the target location and machine-readable instructionssuch that, when executed by the navigation module processor, cause theprocessor to perform a method comprising: identifying a location of theUAV with respect to the 3D map; receiving a target location input;identifying the target location with respect to the 3D map; generatingat least one potential route connecting the location of the UAV and thetarget location; assigning to at least one potential route an evaluationscore according to at least one route assessment criterion; selectingthe potential route having the highest evaluation score as a preferredroute; and transmitting the preferred route to the UAV; and wherein theUAV comprises a UAV memory and at least one of a UAV processor or a UAVmicrocontroller in communication with the UAV memory, the UAV memorystoring machine-readable instructions such that, when executed by theUAV processor or UAV microcontroller, cause the UAV processor or UAVmicrocontroller to perform a method comprising: receiving a preferredroute from the navigation module; and activating propulsion means of theUAV to maneuver the UAV according to the preferred route.

In some embodiments, the system further comprises a user device incommunication with the navigation module and the UAV. In furtherembodiments, the user device transmits the target location input to thenavigation module after receiving a user input. In some embodiments, theUAV further comprises at least one scouting sensor, and wherein themachine-readable instructions stored on the UAV memory, further instructthe UAV processor or UAV microcontroller to: acquire sensor data fromthe at least one scouting sensor and transmit at least a portion of thesensor data to the user device. In further embodiments, the at least onescouting sensor is selected from the group consisting of a camera, aninfrared camera, a microphone, an acoustic sensor, a LiDAR sensor, anultrasound, a sonar, a radar, a gyroscope, an electrochemical toxic gassensor, a thermometer, a humidity sensor, a proximity sensor, abarometric air pressure sensor, a radiation sensor, or a combinationthereof.

In some embodiments, the UAV and the navigation module are incommunication by at least one of: cellular, Wi-Fi, radio frequency,infrared frequency, optical systems, laser systems, or satellitenetworks. In other embodiments, the UAV and the navigation module are incommunication by at least two of: cellular, Wi-Fi, radio frequency,infrared frequency, optical systems, laser systems, or satellitenetworks. In some embodiments, the UAV, the navigation module, and userdevice are in communication by at least one of: cellular, Wi-Fi, radiofrequency, infrared frequency, optical systems, laser systems, orsatellite networks. In other embodiments, the UAV, the navigationmodule, and user device are in communication by at least two of:cellular, WiFi, radio frequency, infrared frequency, optical systems,laser systems, or satellite networks.

In some embodiments, the 3D map comprises at least one of LiDAR data orphotogrammetric calculations. In other embodiments, the 3D map furthercomprises zone indicator tags comprising at least one of: geofencedno-fly zones, drop-off or landing zones, collision risk indicators,weather risk indicators, environment risk indicators, or a combinationthereof.

In some embodiments, the navigation module is physically attached to theUAV. In other embodiments, the navigation module is electronicallyintegrated into and in circuit communication with the UAV. In furtherembodiments, the navigation module is physically separate from the UAV.In other embodiments, the navigation module is one or more computingdevices on a cloud network system. In still further embodiments, thenavigation module is a virtual machine. In still further embodiments,the user device comprises the navigation module. In still additionalembodiments, the user device is a virtual machine.

In some embodiments, the plurality of potential routes is generatedaccording to at least one route constraint criterion comprising at leastone of: a collision safety buffer, a total route distance or time, amaximum altitude, at least one geofenced no-fly zone, a remainingbattery life of the UAV, or a combination thereof. In some embodiments,the at least one route assessment criterion comprises at least one of: atotal route distance or time, a minimum altitude change, a maximumaltitude, a duration of travel time spent above a predetermined altitudethreshold, collision risk indicators, weather risk indicators,environment risk indicators, or a combination thereof. In otherembodiments, assigning to at least one potential route an evaluationscore according to at least one route assessment criterion furthercomprises analyzing at least one route assessment criterion with anartificial intelligence or machine learning technique.

In some embodiments, the machine-readable instructions stored on thenavigation module, further instruct the navigation module processor to:receive at least one of updated 3D map data, geofenced no-fly zones,drop-off or landing zones, collision risk indicators, weather riskindicators, or environment risk indicators; and store the at least oneof updated 3D map data, geofenced no-fly zones, drop-off or landingzones, collision risk indicators, weather risk indicators, andenvironment risk indicators to the memory of the navigation module. Insome embodiments, the navigation module processor identifies thelocation of the UAV with respect to the 3D map via global coordinatedata.

In some embodiments, the machine-readable instructions stored on thenavigation module memory further instruct the navigation moduleprocessor to: receive flight location data of the UAV during at least aportion of flight of the UAV along the preferred route; compare theflight location data to the preferred route to identify whether a routedeviation has occurred; when the route deviation has been identified,calculate a corrected route connecting the location of the UAV to thetarget location; and transmit the corrected route to the UAV; andwherein the machine-readable instructions stored on the UAV memoryfurther instruct the UAV processor or UAV microcontroller to: receivethe corrected route from the navigation module; and activate propulsionmeans to maneuver the UAV according to the corrected route. In someembodiments, the corrected route is calculated according to at least oneroute constraint criterion comprising at least one of: a collisionsafety buffer, a total route distance or time, a maximum altitude, atleast one geofenced no-fly zone, a remaining battery life of the UAV, ora combination thereof. In other embodiments, the corrected route iscalculated by analyzing at least one route constraint criterion with anartificial intelligence or machine learning technique. In someembodiments, the flight location data comprises global coordinate data.

In some embodiments, the machine-readable instructions stored on thenavigation module memory further instruct the navigation moduleprocessor to: receive flight location data from the UAV during at leasta portion of flight of the UAV; match the location data to a position onthe 3D map; generate at least one suggested exploration route based onat least one exploration criterion comprising at least one of: apredicted scouting sensor detection improvement, a collision safetybuffer, a total route distance or time, a maximum altitude, or acombination thereof; display the at least one suggested explorationroute on a display of the user device; receive a selected explorationroute from user input; and transmit the selected exploration route tothe UAV; and wherein the machine-readable instructions stored on the UAVmemory further instruct the UAV processor or UAV microcontroller to:receive the selected exploration route from the navigation module; andactivate propulsion means of the UAV to maneuver the UAV according tothe selected exploration route. In some embodiments, the at least onesuggested exploration route is further based on at least one routeconstraint criterion comprising at least one of: a collision safetybuffer, a total route distance or time, a maximum altitude, at least onegeofenced no-fly zone, a remaining battery life of the UAV, or acombination thereof. In some embodiments, the flight location datacomprises global coordinate data.

In some embodiments, the machine-readable instructions stored on thenavigation module memory further instruct the navigation moduleprocessor to: assign to at least one suggested exploration route a riskevaluation score according to at least one exploration risk assessmentcriterion comprising at least one of: a minimum altitude change, amaximum altitude, a duration of travel time spent above a predeterminedaltitude threshold, collision risk indicators, weather risk indicators,environment risk indicators, or a combination thereof; and display thecorresponding risk evaluation score for each suggested exploration routeon the display of the user device. In other embodiments, assigning to atleast one suggested exploration route a risk evaluation score furthercomprises analyzing at least one exploration risk assessment criterionwith an artificial intelligence or machine learning technique. In someembodiments, the machine-readable instructions stored on the navigationmodule memory further instruct the navigation module processor to:identify whether at least one suggested exploration route fails to meeta predetermined risk evaluation score threshold; and when anidentification has been made, delete the at least one suggestedexploration route that failed to meet a predetermined risk evaluationscore. In further embodiments, the machine-readable instructions storedon the UAV memory, further instruct the UAV processor or UAVmicrocontroller to: receive a manual override command from the userdevice; and maneuver the UAV according to manual maneuver inputs.

Another aspect of the disclosure herein includes for, in someembodiments, a computer-implemented method for dispatching andnavigating an unmanned aerial vehicle (UAV) to a target location, themethod comprising: identifying a location of a UAV with respect to a 3Dmap; receiving a target location input; identifying the target locationwith respect to the 3D map; generating at least one potential routeconnecting the location of the UAV and the target location; assigning toat least one potential route an evaluation score according to at leastone route assessment criterion; selecting the potential route having thehighest evaluation score as a preferred route; and transmitting thepreferred route to the UAV.

In some embodiments of the method, the 3D map comprises at least one ofLiDAR data or photogrammetric calculations. In other embodiments, the 3Dmap further comprises zone indicator tags comprising at least one of:geofenced no-fly zones, drop-off zones, landing zones, collision riskindicators, weather risk indicators, environment risk indicators, or acombination thereof.

In some embodiments of the method, the plurality of potential routes isgenerated according to at least one route constraint criterioncomprising at least one of: a collision safety buffer, a total routedistance or time, a maximum altitude, at least one geofenced no-flyzone, a remaining battery life of the UAV, or a combination thereof. Insome embodiments, the at least one route assessment criterion comprisesat least one of: a total route distance or time, a minimum altitudechange, a maximum altitude, a duration of travel time spent above apredetermined altitude threshold, collision risk indicators, weatherrisk indicators, environment risk indicators, or a combination thereof.In other embodiments, assigning to at least one potential route anevaluation score according to route assessment criteria furthercomprises analyzing at least one route assessment criterion with anartificial intelligence or machine learning technique. In someembodiments, the location of the UAV is identified with respect to the3D map via global coordinate data.

In some embodiments, the method further comprises: receiving flightlocation data of the UAV during at least a portion of flight of the UAValong the preferred route; comparing the flight location data to thepreferred route to identify whether a route deviation has occurred; whenthe route deviation has been identified, calculating a corrected routeconnecting the location of the UAV to the target location; andtransmitting the corrected route to the UAV. In some embodiments, thecorrected route is calculated according to at least one route constraintcriterion comprising at least one of: a collision safety buffer, a totalroute distance or time, a maximum altitude, at least one geofencedno-fly zone, a remaining battery life of the UAV, or a combinationthereof. In some embodiments, the flight location data comprises globalcoordinate data. In other embodiments, the corrected route is calculatedby analyzing at least one route constraint criterion with an artificialintelligence or machine learning technique.

In some embodiments, the method further comprises: receiving flightlocation data from the UAV during at least a portion of flight of theUAV; matching the location data to a position on the 3D map; generatingat least one suggested exploration route based on at least oneexploration criterion comprising at least one of: a predicted scoutingsensor detection improvement, a collision safety buffer, a total routedistance or time, a maximum altitude, or a combination thereof;displaying the at least one suggested exploration route on a display ofa user device; receiving a selected exploration route from user input;and transmitting the selected exploration route to the UAV. In someembodiments, the at least one suggested exploration route is furtherbased on at least one route constraint criterion comprising at least oneof: a collision safety buffer, a total route distance or time, a maximumaltitude, at least one geofenced no-fly zone, a remaining battery lifeof the UAV, or a combination thereof. In some embodiments, the flightlocation data comprises global coordinate data.

In some embodiments, the method further comprises: assigning to at leastone suggested exploration route a risk evaluation score according to atleast one exploration risk assessment criterion comprising at least oneof: a minimum altitude change, a maximum altitude, a duration of traveltime spent above a predetermined altitude threshold, collision riskindicators, weather risk indicators, environment risk indicators, or acombination thereof; and displaying the corresponding risk evaluationscore for each suggested exploration route on the display of the userdevice. In other embodiments, assigning to at least one suggestedexploration route a risk evaluation score further comprises analyzing atleast one exploration risk assessment criterion with an artificialintelligence or machine learning technique.

In some embodiments, the method further comprises: identifying whetherat least one suggested exploration route fails to meet a predeterminedrisk evaluation score threshold; and when an identification has beenmade, deleting the at least one suggested exploration route that failedto meet a predetermined risk evaluation score.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing is a summary, and thus, necessarily limited in detail. Theabove-mentioned aspects, as well as other aspects, features, andadvantages of the present technology are described below in connectionwith various embodiments, with reference made to the accompanyingdrawings.

FIG. 1 illustrates a block diagram of various components of oneembodiment of the system.

FIGS. 2A-C illustrate block diagrams of various embodiments of thesystem components.

FIG. 3 illustrates a cartoon of one embodiment of the system during anintended use.

FIG. 4 illustrates a method for generating a preferred route for a UAVto a target location.

FIG. 5 illustrates a method for monitoring the flight of a UAV to atarget location for route deviations and correcting route deviationswhen they occur

FIG. 6 illustrates a method for monitoring the position of a UAV inflight, generating and suggesting exploration maneuvers to a user, andexecuting upon the selected maneuvers.

The illustrated embodiments are merely examples and are not intended tolimit the disclosure. The schematics are drawn to illustrate featuresand concepts and are not necessarily drawn to scale.

DETAILED DESCRIPTION

The foregoing is a summary, and thus, necessarily limited in detail. Theabove-mentioned aspects, as well as other aspects, features, andadvantages of the present technology will now be described in connectionwith various embodiments. The inclusion of the following embodiments isnot intended to limit the disclosure to these embodiments, but rather toenable any person skilled in the art to make and use the contemplatedinvention(s). Other embodiments may be utilized and modifications may bemade without departing from the spirit or scope of the subject matterpresented herein. Aspects of the disclosure, as described andillustrated herein, can be arranged, combined, modified, and designed ina variety of different formulations, all of which are explicitlycontemplated and form part of this disclosure.

Disclosed herein are systems and methods for dispatching and navigatinga UAV. In many embodiments, the system and methods allow for thesemiautonomous navigation of a UAV to a target location of interest,such as one where a disaster or emergency is occurring (e.g., awildfire, a building fire, a flood, etc.) with greater ease of use thanthat of current systems. Utilizing a navigation module storing at leastthree-dimensional (3D) map data in many embodiments, the systems andmethods allow for a much more “hands-off” approach to dispatching andnavigating a UAV while still avoiding obvious crash hazards and otherflight limitations as well as enabling and assisting a user to inputexplorative and alternative flight maneuvers. In some embodiments, theUAV used can be a commercially available drone modified with specifichardware. In other embodiments, the UAV can be custom built to performthe features described herein.

As discussed herein, the system and methods can be used for emergencyand disaster response, but they can be additionally or alternativelyused for any suitable application where the semiautonomous navigation ofa UAV is desired, such as in aerial photography, delivery services, andrecreational or military purposes. As used herein, the terms “UAV” and“drone” will be considered synonymous and can be used interchangeablythroughout.

In many embodiments, the devices and methods herein employ the use ofcoordinates from one or more global or regional coordinate systems(e.g., a satellite navigation system). These systems include, but arenot limited to, those of the Global Positioning System (GPS), the GlobalNavigation Satellite System (GLONASS), the BeiDou Navigation SatelliteSystem (BDS), Galileo, the Quasi-Zenith Satellite System (QZSS), and theIndian Regional Navigation Satellite System (IRNSS), as well as anyother present or future systems that provide an express coordinate for agiven position about the earth with or without the use of satellites. Asused herein, the term “global coordinate” is intended to refer to acoordinate or equivalent thereof from any of the above or similarsystems. In many embodiments, the global coordinates will be GPScoordinates, but one of skill in the art will appreciate that the use ofthe term “GPS coordinate” herein is not limiting, at least for thereasons stated above.

Systems and Devices

The system functions to dispatch and navigate a UAV to a targetlocation. As shown in FIG. 1 , the system 100 can comprise, in manyembodiments, a UAV 102 in communication 125 with a navigation module152. The UAV 102 can comprise a UAV processor or microcontroller 104 incommunication with a UAV memory 106 that can store variousmachine-readable instructions executable by the UAV processor ormicrocontroller 104. The UAV 102 can additionally comprise propulsionmeans (not shown), such as any number of rotors or engines, powersupplies (not shown) to power the propulsion means, a housing (notshown) to provide structural strength to the UAV, and all the wiring andelectronic components necessary to complete an operable UAV asappreciated by those of skill in the art. The UAV memory 106 can storevarious machine-readable instructions executable by the UAV processor ormicrocontroller 104 such that the UAV 102 is able to operate itspropulsion means in a controlled manner appreciated by those of skill inthe art in addition to the specific tasks as described herein.

In further embodiments, the UAV 102 can comprise at least one scoutingsensor 108 that collects various data about its environment. Scoutingsensors 108 can include, but are not limited, to a camera, an infraredcamera, a microphone, an acoustic sensor a light detecting and ranging(LiDAR) sensor, an ultrasound, a sonar, a radar, a gyroscope, anelectrochemical toxic gas sensor, a thermometer, a humidity sensor, aproximity sensor, a barometric air pressure sensor, a radiation sensor,or a combination thereof. The at least one scouting sensor 108 cancollect data and store it locally on the UAV memory 106. In otherembodiments, the UAV can transmit the data collected by the at least onescouting sensor 108 to the navigation module 152 or to another device,such as a user device (not shown). In certain embodiments, the UAV cantransmit the data collected by the at least one scouting sensor 108 toone or more devices that are a part of a cloud computing system asdescribed herein. In some embodiments, the cloud computing system canrun one or more virtual machines or components.

In many embodiments, the navigation module 152 comprises a navigationmodule processor 154 and a navigation module memory 156 that storesmachine-readable instructions executable by the navigation moduleprocessor 154 as well as 3D map data 160. In some embodiments, the 3Dmap data 160 can be a LiDAR map (e.g., a LiDAR topographic map) of anarea. In other embodiments, the 3D map data 160 can include additionaldata, such as that of photogrammetry and other digital visioncalculations that generate topographic and structural information. The3D map data 160 can account for one or more of: the ground surfacetopography as well as the extent and height of tree cover and otherobstructions (e.g., telephone poles, streetlights, traffic lights, etc.)in various embodiments. In some embodiments, the 3D map data includes across-reference or alignment of LiDAR map data with global coordinates.In these embodiments, a global coordinate can be used to lookup orreturn one or more of the LiDAR topography, satellite imagery, or otherassociated data of a given location. In some embodiments, the navigationmodule can be a virtual machine or can include one or more virtualcomponents.

In some embodiments, the 3D map data 160 further includes zone indicatortags that assign a value readable and manipulable by the system asdescribed herein. The zone indicator tags represent other considerationsuseful for navigating a UAV through or around certain regions of the 3Dmap data 160. In some embodiments, these zone indicator tags caninclude, but are not limited to, geofenced no-fly zones, drop-off orlanding zones, collision risk indicators, weather risk indicators,environment risk indicators, or a combination thereof. For example, ageofenced no-fly zone indicator tag can inform the navigation moduleduring its calculations as discussed below, that the UAV should notenter that area (e.g., the area marks an airport where it is hazardousto fly a UAV). A geofenced no-fly zone can also set an altitude maximumin some embodiments, meaning that a UAV is allowed to fly within acertain region as long as it remains beneath a predetermined altitude. Ageofenced no-fly zone, in some embodiments, can be a temporary orpermanent. In various embodiments, geofenced no-fly zones can beincorporated automatically from Notice to Airmen (NOTAM) messages orTemporary Flight Restrictions (TFR) from relevant aviation andgovernment authorities. A drop-off or landing zone indicator tag caninform the navigation module of an area predetermined as safe todrop-off materials (e.g., a package for delivery, supplies, a life vest,etc.) or on which to land. In many embodiments, a drop-off or landingzone indicator tag denotes a region that is both generally free ofhazards and sufficiently open and accessible for a UAV. Example areasthat can be tagged with a drop-off or landing zone indicator taginclude, but are not limited to, rooftops of some buildings and openfields. A collision risk indicator, for example, can denote a region inwhich a greater risk for accidental collision exists but is notadequately represented in the 3D map data 160 alone, in certainembodiments. For example, areas with lots of electrical or telephonewires or thin tree branches can be marked with a collision riskindicator. A weather risk indicator, in many embodiments, can mark wherehazardous weather conditions (e.g., very strong winds, hail, lightning,etc.) are currently present and thus represent a risk to the UAV. Anenvironment risk indicator can, in many embodiments, represent any otherrisk inherent to the specific location of the 3D map data 160 notalready described. For example, an area currently experiencing awildfire could be labeled with an environment risk indicator to denotean area of high heat or low visibility due to the smoke. In certainembodiments, the environment risk indicator can also be used to mark azone in which the 3D map data 160 is suspected to be deficient,inaccurate, and/or changed since the collection of the 3D map data 160(e.g., a section of a forest after it has burned). In some embodiments,the collision risk indicator, weather risk indicator, and/or environmentrisk indicator can comprise a scalar value describing a greater orlesser risk compared to others of its type. In other embodiments, theabove zone indicator tags can be all collectively or in subsets mergedinto single values to represent a combined risk for the UAV in that areaof the 3D map data 160 that can be considered by the system 100 during aroute calculation, as described herein. Additional types of zoneindicator tags can be included without deviating from the scope of thisdisclosure. In various embodiments, 3D map data (i.e., LiDAR orphotogrammetry calculations with or without zone indicator tags) can beprovided to the system either by manual input and/or automatic uploadincluding from integrated third-party systems.

In some embodiments, the system 100 can store common route data as partof the 3D map data 160. In these embodiments, common route datarepresent a previously calculated preferred route (e.g., see FIG. 4 )between a frequently used starting point and a frequently used targetlocation for the UAV 102. In this way, the system 100 can save time byutilizing common route data to provide the previously calculatedpreferred route as a potential route when the navigation module 152identifies relevant starting points and target locations instead ofregenerating the previously used route. In many embodiments, thepotential route provided by the common route data will be subsequentlyanalyzed for transient 3D map data (e.g., weather and environment riskindicators) and possibly adjusted or rejected before being transmittedto the UAV 102 as the preferred route for the given occasion.

The navigation module memory 156, in many embodiments, is capable ofreceiving updated 3D map data 160. In many of these embodiments, whenthe navigation module memory 156 receives updated 3D map data 160, itappropriately uses the updated data 160 for any future operations unlessotherwise instructed to retrieve and utilize outdated data 160. Theupdated 3D map data can include new LiDAR map portions (e.g., new LiDARscans of buildings newly constructed or forests cleared since a previousscan), new photogrammetric calculations, as well as new or updated zoneindicator tags. In some embodiments, updates of certain portions of the3D map data 160 can happen comparatively infrequently (e.g., new LiDARscans) compared to other portions of the 3D map data (e.g., weatherindicator tags can update on a live update schedule, in someembodiments). In other embodiments, new drop-off or landing zoneindicator tags can be provided live as updated 3D map data while a UAV102 is en route to a nearby location. In further embodiments, the atleast one scouting sensor 108 can collect new 3D map data 160 while enroute to a target location. In these embodiments, this newly collected3D map data 160 can be immediately analyzed by the navigation module 152to generate updated or alternative routes for the UAV 102. In someembodiments, artificial intelligence (AI) or machine learning (ML)techniques can be employed to analyze the new 3D map data 160 (e.g.,visual images of a location) collected by the at least one scoutingsensor 108 en route to further inform decisions by the navigation module152. For example, the scouting sensor 108, in one embodiment, cancollect new LiDAR scans that reveal to the navigation module 152obstructions or clearings previously unknown. In another example, thescouting sensor 108 can detect and monitor weather or environmentalconditions that contribute new or updated weather or environmental riskindicators to the 3D map data.

The UAV 102 and the navigation module 152 are in communication 125 witheach other. In certain embodiments, each are also in communication withone or more user devices (not shown). In some embodiments, a user devicecan be a virtual user device. A wide variety of user devices can beemployed, including those with augmented reality (AR) and virtualreality (VR) capabilities as descried herein. In further embodiments, asingular navigation module 152 can be in communication with a pluralityof UAVs 102. In some embodiments, a singular navigation module 152 canbe in communication with and direct a plurality of UAVs 102 to the sametarget location. In other embodiments, a singular navigation module 152can be in communication with and direct a plurality of UAVs to uniquetarget locations. In still further embodiments, a UAV 102 can be incommunication 125 with one or more separate UAVs 102. In someembodiments, the UAV 102 and the navigation module 152 are incommunication 125 with each other by at least one of cellular, Wi-Fi,radio frequency, infrared frequency, optical systems, laser systems, orsatellite networks. In other embodiments, the plurality of UAVs 102 isin communication 125 with each other by at least one of cellular, Wi-Fi,radio frequency, infrared frequency, optical systems, laser systems, orsatellite networks. When it is said herein that components of the system100 (e.g., a UAV 102 and navigation module 152) are in communicationwith each other, one of skill in the art will appreciate that thecomponents then comprise the necessary hardware and store any necessarymachine-readable instructions in order to utilize the communicationprotocol or method. In this manner, the UAV 102 and the navigationmodule 152 can be considered to each comprise a communications module(not shown) in certain embodiments where appropriate. In otherembodiments, a communication protocol or method can be selected based onthe 3D map, the zone indicator tags, communication protocolavailability. In some embodiments, if one protocol is not available,communication can be switched to another protocol or method.

In other embodiments, the UAV 102 and the navigation module 152 are incommunication 125 with each other by at least two of cellular, Wi-Fi,radio frequency, infrared frequency, optical systems, laser systems, orsatellite networks. By being in communication over a plurality ofcommunication protocols or methods, a redundancy is built into thesystem 100 in case any portion of the system 100 takes damage.

In some embodiments, the UAV 102, the navigation module 152, and one ormore user devices are in communication 125 with each other by at leastone of cellular, Wi-Fi, radio frequency, infrared frequency, opticalsystems, laser systems, or satellite networks. In other embodiments, theUAV 102, the navigation module 152, and one or more user devices are incommunication 125 with each other by at least two of cellular, Wi-Fi,radio frequency, infrared frequency, optical systems, laser systems, orsatellite networks. Furthermore, in some embodiments various componentsof the system 100 are in communication simultaneously over a pluralityof communication protocols or methods for different purposes. Forexample, satellite networks can be used for the one-way transmission ofglobal coordinate information while a cellular network is used to sendcommands and receive data from the at least one scouting sensor 108. Inother embodiments, location data regarding the position of the UAV 102and/or one or more user devices can be sent to the navigation module 152by a communication protocol other than a satellite network to allow thesystem 100 to operate in contingencies wherein an appropriate satellitenetwork cannot be reached. In further embodiments, RFID tags and readerscan be implemented on the UAV 102 and other devices or objects (e.g., auser device, equipment or a package for delivery, other UAVs, etc.) fortheir detection and identification by the UAV 102 or navigation module152. In some of these embodiments, the RFID tags and readers canfacilitate the ability for a plurality of UAVs 102 to fly in formationor for a singular UAV to identify one package for delivery among many.

In some embodiments, the UAV 102 and the navigation module 152 arephysically separate devices. In these embodiments, they can thencommunicate 125 via wireless systems as described above. In otherembodiments, the navigation module 152 is physically attached to the UAV102. This can be by mechanically fastening a housing (not shown)containing the navigation module 152 to the UAV 102. In certain versionsof this embodiment, the navigation module 152 can be adapted to draw itselectrical power from a separate power supply (not shown) or from thesame one or more power supplies (not shown) of the UAV 102. In someembodiments where the navigation module 152 is fastened to the UAV 102,the UAV 102 and navigation module 152 can still communicate 125 viawireless systems as described above.

In other embodiments, the navigation module 152 is electronicallyintegrated into and in circuit communication with the UAV 102. Incertain embodiments, the terms “electronically integrated” and “incircuit communication” are unified in a singular electronic system,sharing at least a portion of their features and circuitry. For example,in an embodiment wherein the navigation module 152 is electronicallyintegrated and in circuit communication with the UAV 102 a singularprocessor or microcontroller that performs all the steps described ofboth the UAV processor or microcontroller 104 and the navigation moduleprocessor 154 as described herein. In similar embodiments, there can bea singular memory that stores the machine-readable instructions to beexecuted by the UAV processor or microcontroller 104 and those by thenavigation module processor 154 in addition to storing the 3D map data160. Having the navigation module 152 electronically integrated and incircuit communication with the UAV 102 can dramatically reduce delay incommunication times between the two, in many embodiments. In embodimentswherein the navigation module 152 is electronically integrated into andin circuit communication with the UAV 102, the UAV 102 can be consideredto comprise the navigation module 152.

In still other embodiments, the navigation module 152 comprises aplurality of navigation module processors 154 and navigation modulememories 156 operating as a cloud computing system in communication 125with the UAV 102 and, in some embodiments, a user device (not shown). Inthese embodiments, the hardware of the navigation module 152 is notexposed to the same hazards as the UAV 102 during a flight of the UAV102. Furthermore, the use of a cloud computing system can expedite thespeed of route calculations of the navigation module 152 as describedherein in certain embodiments. In some embodiments, the cloud computingsystem can include various one or more virtual machines or virtualcomponents. In these embodiments, the virtual machines and componentscan emulate various hardware components and software operations thatfacilitate the performance of the system and methods described herein,including, but not limited to, the navigation module and user device. Instill further embodiments, the navigation module can employ artificialintelligence (AI) and/or machine learning (ML) techniques, including butnot limited to those involving computer vision, image processing, andpattern discovery, recognition, and classification as well as thoseemployed for route optimization. In some embodiments, a singlenavigation module 152 can be in communication 125 with and navigate aplurality of UAVs 102.

A variety of user devices can be employed across many embodiments. Insome embodiments, the user device can be a tablet, a mobile device(e.g., a mobile phone), a personal computer, a laptop computer, anaugmented reality (AR) device, a virtual reality (VR) device, a wearabledevice (e.g., glasses, a watch, etc.), etc. In other embodiments, theuser device can be a virtual machine (e.g., a virtual user device). Insome embodiments, the user device comprises a display that displaysvarious information regarding the UAV 102, including but not limited toa location of the UAV 102, one or more actual or potential flight pathsof the UAV 102, as well as any data collected by the scouting sensor 108of the UAV. In some embodiments, the display can include the display ofan AR device, such as a pair of glasses with an AR heads-up display(HUD) or a similar HUD displayed onto the windshield of a vehicle. Inother embodiments, the display can include the display of a VR device,such as a pair of virtual reality goggles with or without additionalperipheral devices. In various embodiments, a user can input user inputsvia a VR or AR user device by performing physical gestures with orwithout the use of additional wearable or peripheral devices such asgloves containing markers or cameras. In other embodiments, the userdevice is capable of receiving user input from the user and submittingit to at least one of the UAV 102 and/or the navigation module 152. Thisinput (e.g., voice, text, haptic, etc.) can include, but is not limitedto, a selection of a target location and/or a selection of one or moreflight paths of the UAV, as described herein. In still furtherembodiments, the input can include a manual override command andsubsequent manual maneuver inputs that allow a user to navigate andmaneuver the UAV 102 manually via the user device. In some embodiments,a single user device can provide one or more of the above and/oradditional functions. In other embodiments, multiple user devices, eachcapable of a subset of one or more of the above or additional functions,are in communication with the UAV 102 and navigation module 152 for thepurposes of accomplishing the various technological features of the userdevice as described herein. For example, a first user device cancomprise a display for displaying information regarding the UAV 102 andcan transmit flight path selections to the UAV 102 or navigation module152 while a second user device is capable of sending a manual overridecommand and manual maneuver inputs to the UAV 102 and/or navigationmodule 152.

FIGS. 2A-C depict various embodiments of the system comprising a UAV, anavigation module, and at least one user device. As shown in FIG. 2A,the system 200 a can comprise physically separate UAV 202 a, navigationmodule 204 a, and user device 206 a, all in communication 210 a witheach other in many embodiments. In certain embodiments, the user device206 a can comprise a plurality of user devices. In some embodiments, thenavigation module 204 a can be a cloud computing system, as describedherein. In other embodiments, the navigation module 204 a can bephysically attached to the UAV 202 a but not electronically integratedor in circuit communication with it as described herein. In theseembodiments, separate channels of communication 210 a can be requiredbetween all three of the UAV 202 a, navigation module 204 a, and theuser device 206 a. Any two of the UAV 202 a, navigation module 204 a,and user device 206 a can be in communication 210 a with each other byat least one of cellular, Wi-Fi, radio frequency, infrared frequency,optical systems, laser systems, or satellite networks, as describedherein in some embodiments. In other embodiments, any two of the UAV 202a, navigation module 204 a, and user device 206 a can be incommunication 210 a with each other by at least two of cellular, Wi-Fi,radio frequency, infrared frequency, optical systems, laser systems, orsatellite networks, as described herein. In certain embodiments, allthree of the UAV 202 a, navigation module 204 a, and user device 206 acan be in communication 210 a with each other by at least one ofcellular, Wi-Fi, radio frequency, infrared frequency, optical systems,laser systems, or satellite networks, as described herein in someembodiments. In still further embodiments, all three of the UAV 202 a,navigation module 204 a, and user device 206 a can be in communication210 a with each other by at least two of cellular, Wi-Fi, radiofrequency, infrared frequency, optical systems, laser systems, orsatellite networks, as described herein.

FIG. 2B shows an alternative embodiment of the system 200 b where theUAV 202 b comprises the navigation module 204 b as described herein.Because the navigation module 204 b is electronically integrated and incircuit communication with the UAV 202 b, the system 200 b can beconsidered to only need to account for communication 210 b between theuser device 206 b and the UAV 202 b. In certain embodiments, the userdevice 206 b can be a plurality of user devices. In some embodiments,the UAV 202 b and the user device 206 b can be in communication 210 bwith each other by at least one of cellular, Wi-Fi, radio frequency,infrared frequency, optical systems, laser systems, or satellitenetworks as described herein. In other embodiments, the UAV 202 b andthe user device 206 b can be in communication 210 b with each other byat least two of cellular, Wi-Fi, radio frequency, infrared frequency,optical systems, laser systems, or satellite networks as describedherein.

FIG. 2C shows an alternative embodiment of the system 200 c where thenavigation module 204 c is electronically integrated and in circuitcommunication with the user device 206 c. In these embodiments, the userdevice 206 c can be considered to comprise the navigation module 204 c.In some embodiments, the user device 206 c can be a plurality of userdevices, only one of which comprising the navigation module 204 c. Instill other embodiments, the user device 206 c can be a plurality ofuser devices, each comprising a portion of the technological features ofthe navigation module 204 c, as described herein. In these embodiments,the plurality of user devices can all be in communication 210 c witheach other in addition to being in communication 210 c with the UAV 202c. In some embodiments, the UAV 202 c and the user device 206 c can bein communication 210 c with each other by at least one of cellular,Wi-Fi, radio frequency, infrared frequency, optical systems, lasersystems, or satellite networks, as described herein. In otherembodiments, the UAV 202 c and the user device 206 c can be incommunication 210 c with each other by at least two of cellular, Wi-Fi,radio frequency, infrared frequency, optical systems, laser systems, orsatellite networks, as described herein.

In various embodiments of FIGS. 2A-2C, one or more the UAV 202 a, 202 b,202 c, the navigation module 204 a, 204 b, 204 c, and the user device206 a, 206 b, 206 c can be configured to be compatible with variousthird-party software modules and hardware components, allowing for thecustomizability of the system 200 towards specific use cases.

FIG. 3 depicts a cartoon of one embodiment of the system 300 during anintended use. A user 301, such as an incident commander of an emergencyresponse team, would like to deploy a UAV 302 to a target location 303where an emergency is currently occurring in order to acquire data fromat least one scouting sensor (not shown) on the UAV 302. Via a userdevice 306 that is in communication 310 with the UAV 302 and anavigation module 304, the user 301 can input the target location 303into the system 300. Using its 3D map data (not shown), the navigationmodule 304 can calculate a preferred route (i.e., a flight path) asdescribed herein that dispatches the UAV 302 to the target location 303.As described herein, the navigation module 304 can additionallyautonomously check to see if the UAV 302 has deviated from the preferredroute and correct course accordingly. Furthermore, as described herein,the navigation module 304 can suggest exploratory routes to the user 301via the user device 306 in various embodiments.

Methods

FIG. 4 depicts an embodiment of a method 400 for generating a preferredroute for a UAV to a target location. As used herein, the term “route”and “flight path” are considered equivalent and can be usedinterchangeably. In many embodiments, machine-readable instructionsstored on the navigation module memory can cause the navigation moduleto perform the method 400 of FIG. 4 . As shown, an embodiment of thismethod 400 includes identifying a location of the UAV with respect tothe 3D map in block S402, receiving a target location input in blockS404, identifying the target location with respect to the 3D map inblock S406, generating at least one potential route connecting thelocation of the UAV and the target location in block S408, assigning toat least one potential route an evaluation score according to routeassessment criteria in block S410, selecting the potential route havingthe highest evaluation score as a preferred route in block S412, andtransmitting the preferred route to the UAV in block S414. The method400 is used for disaster and emergency response teams but canadditionally, or alternatively, be used for any suitable applications,where the semiautonomous navigation of a UAV is desired. In manyembodiments, the generation of the preferred route can be considered anoptimization problem towards risk analysis. One of skill in the art willappreciate the variety of specific computational methodologies that canbe employed including, but not limited to, simplex, fuzzy logic, andSymbiotic Organisms Search (SOS) methodologies. In still furtherembodiments, artificial intelligence (AI) and/or machine learning (ML)techniques can be utilized to perform the optimization calculations. Theblocks of method 400 and subsequent methods described herein present anorganization of the analyses required and is not intended to be limitedby any specific computational methodology.

In block S402, the method 400 includes identifying a location of the UAVwith respect to the 3D map. In order to calculate a route for the UAV toa target location, the navigation module can begin by identifying acurrent or starting location of the UAV. In some situations, the UAV canbe grounded on a landing pad or any solid surface, whereas in othersituations, the UAV can be currently in flight, either moving to a newlocation (e.g., the target location, on a scheduled patrol, etc.) orholding a stable position in the air. In certain embodiments, the UAV isdeployed from a consistent starting position, such as a defined landingpad at a base of operations of an emergency response team. In otherembodiments, a user can first transport (e.g., by car) the UAV to aunique starting point from which to begin a use of the system. In stillother embodiments, the UAV can be deployed from a moving vehicle, suchas a fire engine en route to an incident. In many embodiments, themethod 400 employs global coordinate tracking technology and/or similarsystems as appreciated by those of skill in the art, to assist in theperformance of block S402; however, in these embodiments, the globalcoordinates or similar identifier can then be cross-referenced orotherwise further identified with respect to the 3D map.

In block S404, the method 400 includes receiving a target locationinput. In many embodiments, the target location input is received from auser device that collected a user input. For example, this can be from amanual user input via a GUI on a display of a user device (e.g., atablet or mobile device) or via a phone call on a mobile phone toemergency services in which the phone's global coordinates are directlytransmitted with the phone call. In other embodiments, the targetlocation input can be received from an automated system in communicationwith the navigation module, such as a computer-aided dispatch (CAD)system. Because a user must originally have inputted the target locationinto the CAD system, the CAD system can also be considered a user devicein some embodiments. In still further embodiments, the target locationinput can be received from a sensor on any device integrated into thesystem either directly or via a CAD or equivalent system. For example, asmoke detector adequately integrated into the system (e.g., into an“Internet of Things” arrangement) can send its global coordinates upondetecting smoke. Other examples can include, but are not limited tomotion detectors, microphones, thermometers, and other sensors capableof detecting a situation in which the deployment of a UAV to itslocation is desired. In these embodiments, any device with such a sensorcan be considered a user device because a user necessarily had toarrange the device to do so. In various embodiments, third party systemsthat can perform the detection of emergencies by predefined parametersand transmission of subsequent information can be integrated into thesystem of the disclosure herein to provide a target location input.Additionally, third party systems that allow for the direct transmissionof a target location input (or the equivalent thereof) from a phone callto a CAD can also be integrated into the system of the disclosure hereinin certain embodiments. In many embodiments, the target location can bereceived as global coordinates or a similar geographical locationreference technique or format. In some embodiments, the target locationcan be a plurality of locations to be visited by a UAV in sequence.

In block S406, the method 400 includes identifying the target locationwith respect to the 3D map. In certain embodiments, this can includecorrelating a global coordinate or other indicator with a location onthe 3D map. One of skill in the art will appreciate that blocksS402-S404 can be performed in various orders without deviating from thescope of the disclosure.

In block S408, the method 400 includes generating at least one potentialroute connecting the location of the UAV and the target location.Various considerations and techniques, including, but not limited to, AIor ML techniques, can be used to generate the at least one potentialroute. In many embodiments, the at least one potential route considersmovement in all three dimensions, (i.e., changes in latitude, longitude,and altitude) as well as 3D map data such that the at least onepotential route does not intersect with known obstacles (e.g., trees,buildings, hillsides, etc.). In embodiments where there is a pluralityof target locations, the at least one potential route can include asequential visiting of the plurality of target locations. In otherembodiments having a plurality of target locations, the at least onepotential route can be a “patrol” route that continuously loops the UAVthrough at least a portion of the plurality of target locations.

In further embodiments, the at least one potential route is generatedaccording to additional route constraint criteria comprising at leastone of: a collision safety buffer, a total route distance or time, amaximum altitude, at least one geofenced no-fly zone, a remainingbattery life of the UAV, or a combination thereof.

In some embodiments, a collision safety buffer defines a maximumdistance between potential routes and a known obstacle, as described bythe 3D map data. Although a LiDAR-generated 3D map can be very accuratein certain embodiments, it can be of interest to some users in somesituations to further reduce the risk of collision by limiting the UAV'smovement to within a certain buffered distance of the known obstacles.In some embodiments, the collision safety buffer can be from about 0.25meters to about 100 m. In some further embodiments, the collision safetybuffer can be from about 0.25 m to about 50 m. In other embodiments, thecollision safety buffer can be from about 0.25 m to about 10 m. In stillother embodiments, the collision safety buffer can be from about 0.25meters to about 5 m. In further embodiments, the collision safety buffercan be from about 0.25 meters to about 2.5 m. In still furtherembodiments, the collision safety buffer can be about 1 m. In someembodiments, different types of obstacles captured by the 3D map can becategorized (e.g., trees, buildings, etc.), and a different collisionsafety buffer can be applied to each category of obstacle. For example,route constraint criteria can allow for the at least one potential routeto pass within 1 m of a building but only within 5 m of a tree, in oneembodiment. In other embodiments, different species of trees can bedefined as separate categories of obstacles and thus assigned differentcollision safety buffers to account for variance in growth since theprevious LiDAR scan or other update of the 3D map.

A total route distance or time, as a route constraint criterion, placesa limitation on the total allowed travel distance or time for the atleast one potential route in some embodiments. In certain embodiments,this limitation can be useful in order to avoid or reduce wear-and-tearand/or reliability concerns of certain UAVs. For example, it could beknown that a specific UAV model suffers notable rotor damage for flightssurpassing a flight time of five hours; thus, this route constraintcriterion would limit the system from generating a potential route thatrisked this wear. In some embodiments, the total allowed travel distanceor time can vary for different models of UAVs. The term “total routedistance” and “total route time” are intended to mean the distance ortime required to maneuver the UAV from a starting position to the targetlocation. In certain embodiments, however, a suggested exploration routecan be designed to loop back to its starting position. In someembodiments, the total route distance, as a route constraint criterion,can be from about 10 m to about 10 km. In other embodiments, the totalroute distance can be from about 100 m to about 10 km. In still otherembodiments, the total route distance can be from about 250 m to about10 km. In still some further embodiments, the total route distance canbe from about 250 m to about 5 km. In further embodiments, the totalroute distance can be from about 100 m to about 5 km. In still furtherembodiments, the total route distance can be from about 100 m to about1000 m. In additional embodiments, the total route distance can be fromabout 10 m to about 500 m. In some embodiments, the total route time, asa route constraint criterion, can be from about 1 minute to about 12hours. In other embodiments, the total route time can be from about 5minutes to about 4 hours. In still other embodiments, the total routetime can be from about 30 minutes to about 4 hours. In furtherembodiments, the total route time can be from about 1 hour to about 2hours. In still further embodiments, the total route time can be fromabout 1 hour to about 8 hours. In additional embodiments, the totalroute time can be from about 1 hour to about 6 hours. In manyembodiments, the total route distance or time, as a route constraintcriterion, is determined according to the known capacities of the UAVbeing deployed.

A maximum altitude, as a route constraint criterion, places a limitationon the highest altitude the at least one potential route is allowed toreach, in some embodiments. In certain circumstances, some UAV modelsmay not be reliable or operable above certain altitudes, or there may belaws against flying UAVs above certain altitudes in some areas.Therefore, in these embodiments, a maximum altitude route constraintcriterion prevents the generation of a potential route that endangersthe UAV or breaks regional legislation. In many embodiments, the maximumaltitude, as a route constraint criterion, is determined according tothe known capacities of the UAV being deployed.

A geofenced no-fly zone, as a route constraint criterion, excludes thegeneration of a potential route that passes through restricted areas asdetermined by zone indicator tags of the 3D map, as described above insome embodiments. Additionally, as described above, a maximum altitudeconstraint can be alternatively identified as geofenced no-fly zoneswith an appropriate altitude exception, in some embodiments.

A remaining battery life, as a route constraint criterion, considers theprojected flight time available based on the present amount of batterypower of the UAV, and prevents the generation of potential routes thatwould fully deplete the power supply before its arrival to thedestination, in some embodiments. In other embodiments, the remainingbattery life route constraint criterion further considers the durationof the full round trip (i.e., to the location of interest and back tothe starting point of the UAV), thereby preventing the generation of afully-depleting round trip potential route. In still furtherembodiments, the remaining battery life route constraint criterionfurther considers the duration of the full round trip plus apredetermined amount of hovering, surveilling, and/or exploring time atthe target location. In many embodiments, a remaining battery life, as aroute constraint criterion, is determined according to the knowncapacities of the UAV being deployed.

In block S410, the method 400 includes assigning to at least onepotential route an evaluation score according to route assessmentcriteria in many embodiments. In various embodiments, the routeassessment criteria can comprise at least one of a total route distanceor time, a minimum altitude change, a maximum altitude, a duration oftravel time spent above a predetermined altitude threshold, collisionrisk indicators, weather risk indicators, environment risk indicators,or a combination thereof. In many embodiments, the evaluation score fora potential route is a composite of individual scores for each routeassessment criteria applied to the potential route. In some embodiments,the individual scores for each route assessment criteria can be compiledevenly to generate the evaluation score. In other embodiments, theindividual scores can be weighted according to a predetermined metric orfunction. In some embodiments, the predetermined metric or function canbe produced by an AI or ML technique trained on a data set of exemplarysuccessful and unsuccessful routes having various values for one or moreof the above route assessment criteria. For example, a successfullytraversed flight path over a mountain or between buildings that passesthrough or near various zone indicator tags can be used as part of thetraining data set.

A total route distance or time, as a route assessment criterion, morefavorably values potential routes with shorter total travel distances ortimes for a UAV to reach the target location, in many embodiments. Inmany circumstances, the shorter the route, the faster the UAV can arriveat the target location, and the saved time can be critical in certainemergency or disaster situations. Furthermore, reducing the total traveldistance or time of a UAV can help minimize or reduce the expectedwear-and-tear on a UAV over many flights, reducing maintenance andreplacement costs. In various embodiments, the score for a potentialroute's total route distance or time can change linearly, quadratically,geometrically, stepwise or according to another function with increasingdistance or time. In many embodiments, potential routes that have ashorter total distance or time compared to other potential routes to thesame target location will score better for the total route distance ortime route assessment criterion, in many embodiments.

A minimum altitude change, as a route assessment criterion, morefavorably values potential routes with less variance in altitude, inmany embodiments. In some embodiments, maintaining a constant altitudeor a narrow range of altitudes, can be beneficial towards the collectionof consistent and reliable data from one or more scouting sensors of theUAV. In other embodiments, frequent and/or extreme changes in altitudecan cause greater wear-and-tear to a UAV compared to steady flight at aconstant altitude. In various embodiments, the score for a potentialroute's minimum altitude change can change linearly, quadratically,geometrically, stepwise or according to another function with increasingchange in altitude. In many embodiments, potential routes that have aconstant altitude or a narrower range of altitude compared to otherpotential routes to the same target location will score better for theminimum altitude change route assessment criterion.

A maximum altitude, as a route assessment criterion, more favorablyvalues potential routes that stay below a predetermined altitude valuein many embodiments. In certain embodiments, operating at higherelevations introduces a greater risk to the UAV than operating at alower altitude. In various embodiments, the score for a potentialroute's maximum altitude can linearly, quadratically, geometrically,stepwise or according to another function with increasing altitude. Inmany embodiments, potential routes that stay beneath a predeterminedaltitude will score better for the maximum altitude route assessmentcriterion.

Collision risk indicators, weather risk indicators, and environment riskindicators, as route assessment criteria and alone or in any combinationwith each other, more favorably value potential routes with a lesserlikelihood of possible damage to the UAV in many embodiments. Asdescribed above, these indicators serve to quantify various hazards fora UAV that may not have been adequately represented by the LiDAR mapalone and can be assigned to the map as zone indicator tags. In manyembodiments, when a potential route passes through one or more zonestagged with at least one of these risk indicators tags, the valuesrepresenting the risk of those zones can be compiled and considered as aroute assessment criterion. Potential routes that spend less flightdistance or time within hazardous zones tagged with these riskindicators compared to other potential routes to the same targetlocation will score better for the collision risk indicator routeassessment criterion, weather risk indicators route assessmentcriterion, and/or environment risk indicators route assessmentcriterion. In some embodiments, values associated with individualcollision risk indicators, weather risk indicators, and environment riskindicators can be generated by AI or ML techniques trained on data setsof successful and unsuccessful UAV flights through said categories ofrisk indicators.

Considering one or more route assessment criteria, the method 400generates and assigns an evaluation score for at least one potentialroute that represents a potential route's overall preferability andcompatibility with the predetermined parameters of the utilized routeassessment criteria. In block S412, the method 400 includes selectingthe potential route having the highest evaluation score as a preferredroute. The term “highest evaluation score” as used herein is intended toindicate the potential route that best fits the parameters of the routeassessment criteria; however, the specific details of the calculationand ranking can vary without deviating from the scope of thisdisclosure. In some embodiments the “highest evaluation score” can bethe greatest numerical value among the evaluation scores for eachgenerated potential route. In other embodiments, the “highest evaluationscore” can be the lowest numerical value among the evaluation scores foreach generated potential route. Regardless of the specifics (includingbut not limited to the various examples and embodiments disclosedherein), the method selects the potential route having the highestevaluation score as defined herein as a preferred route. In variousembodiments, the method 400 can employ simplex, fuzzy logic, andSymbiotic Organisms Search (SOS) methodologies to generate the preferredroute. In still further embodiments, artificial intelligence (AI) and/ormachine learning (ML) techniques can be utilized to calculate thepreferred route.

In certain embodiments, certain route assessment criteria can be ignoredand/or a potential route having an inferior evaluation score can beselected when urgency override input is received. In many embodiments,urgency override input can be received from user input on a user deviceor from automated systems such as a CAD. For example, the method 400 cangenerate two potential routes through a wildfire to reach a targetlocation of trapped hiker. A first route is much faster but poses agreater risk to the UAV since it must fly through hotter, more hazardoussections of the blaze. The second route is much longer but safer sinceit avoids the particularly threatening areas. In many embodiments, themethod 400 can select the second route, since it would receive a higherevaluation score; however, urgency override input can force the method400 and/or a system performing the method 400 to select the first routeinstead. For example, an incident commander at the scene can decide thatthe saved time in getting the sensors of the UAV to the target locationof the trapped hiker is worth a potentially fatal risk to the UAV, andhe or she can input the urgency override input on a user device. In someembodiments of this example, the method 400 can be considered to beignoring the environment risk indicators as route assessment criteria.In other embodiments of this example, the method 400 can be consideredto be selecting the potential route having an inferior evaluation score.Another illustrative example of a use for urgency override input caninclude to deploy a UAV beyond its ability to safely return, meaning theUAV will likely crash, potentially irrecoverably, while at the targetlocation or on its way back.

In some embodiments, the method includes for checking for common routedata between the location of the UAV and the target location. Asdescribed herein, common route data can store one or more preferredroutes connecting a frequently used starting point and frequently usedtarget location. In this manner, the method 400 can save time byutilizing previously preferred routes from the common route data aspotential routes for the new instance, thereby avoiding regenerating theold routes on each occasion. In many embodiments, the potential routesprovided by the common route data will be subsequently analyzed fortransient factors such as weather or environment risk indicators asdescribed herein.

In block S414, the method 400 includes transmitting the preferred routeto the UAV. In many embodiments, the preferred route is transmitted tothe UAV in a manner that enables the UAV, utilizing its processor ormicrocontroller and memory, to activate its propulsion means in order tomaneuver according to the preferred route to reach the target location.By various embodiments of the method 400, a UAV can be automaticallydispatched to a target location by a simple input of the target locationusing the 3D map data.

FIG. 5 depicts an embodiment of a method 500 for monitoring the flightof a UAV to a target location (or while on an exploratory route, asdescribed herein) for route deviations and correcting route deviationswhen they occur. In many embodiments, machine-readable instructionsstored on the navigation module memory can cause the navigation moduleto perform the method 500 of FIG. 5 . As shown, an embodiment of thismethod 500 includes receiving flight location data of a UAV during atleast a portion of a flight of the UAV along a preferred route in blockS502, comparing the flight location data to the preferred route toidentify whether a route deviation has occurred in block S504. Dependingon whether a route deviation has occurred, the method 500 either returnsto block S502 or advances to block S506. If the method 500 advances, itincludes calculating a corrected route connecting the location of theUAV to the target location in block S508 and transmitting the correctedroute to the UAV in block S510. In still further embodiments, AI and/orML techniques can be utilized to perform various portions of the method500. The blocks of method 500 and subsequent methods described hereinpresent an organization of the analyses required and is not intended tobe limited by any specific computational methodology.

In block S502, the method 500 includes receiving flight location data ofa UAV during at least a portion of a flight of the UAV along a preferredroute. In many embodiments, the preferred route is generated by themethod 400 of FIG. 4 or by the system of any of FIGS. 1-3 . In alternateembodiments, the flight location data is that of a UAV on an exploratoryroute described herein, such as one generated by the method 600 of FIG.6 . In some embodiments, global coordinate tracking and/or similartechnology, as appreciated by those of skill in the art, can be employedto receive flight location data of the UAV's position during a flight.The flight location data can be cross-referenced or correlated withlocations on the 3D map in many embodiments. In some embodiments, flightlocation data can be received only at regular intervals (e.g., 30seconds, 1 minute, 2 minutes, 5 minutes, 7 minutes, 10 minutes, etc.).In other embodiments, flight location data is received as frequently andcontinuously as the employed tracking technology allows.

In block S504, the method 500 includes comparing the flight locationdata to the preferred route to identify whether a route deviation hasoccurred. In many embodiments, when a system generates, identifies, andtransmits a preferred route to a UAV as in the method 400 of FIG. 4 ,the system can store a representation compatible with the receivedlocation data of the preferred route to be taken. In some embodiments,this can be done by saving a series of GPS coordinates correlated withthe 3D map that follows the preferred route. In other embodiments, thiscan be done by saving a series of global coordinates correlated with the3D map data as a series of waypoints connected by vectors, linear orotherwise. The method 500 compares the most recent flight location datapoint to the stored preferred route to determine whether the UAV isstill on the preferred route. Route deviations can occur in a variety ofways. For example, a sudden, strong wind could blow the UAV off course,a bird could accidentally strike the UAV, the UAV may have a mechanical,electrical, or computational defect that prevents or impairs it fromfollowing the preferred route as originally transmitted, etc.

In block S506, the method 500 proceeds to block S508 if the most recentflight location data received does not align with the stored preferredroute (i.e., a route deviation has occurred). If the most recent flightlocation data received does align with the stored preferred route or atleast within a deviation tolerance of it, the method 500 returns toblock S502 to continue to receive additional and subsequent flightlocation data for future comparisons. In some embodiments, the deviationtolerance can be from about 0.25 m to about 10 m. In other embodiments,the deviation tolerance can be from about 0.25 m to about 5 m. Infurther embodiments, the deviation tolerance can be from about 1 m toabout 3 m. In still further embodiments, the deviation tolerance can beabout 2 m. In certain embodiments, specific zones or regions of the 3Dmap can be restricted to narrower or broader deviation tolerancesaccording to zone indicator tags.

In block S508, the method 500 includes calculating a corrected routeconnecting the location of the UAV to the target location. Bycalculating a corrected route, the method 500 seeks to adjust the UAV'sflight path to one that will guide it to the target location. In someembodiments, the corrected route comprises the shortest maneuvernecessary to return it to the preferred route. In other embodiments,such as those wherein a substantial deviation has occurred, the method500 can calculate a new route to the target location with no or partialoverlap to the preferred route. In many embodiments, the calculatedcorrected route of block S508 can be performed according to similar oridentical procedures and considerations of blocks S408-S410 (i.e.,generating at least one corrected route according to route constraintcriteria, and evaluating the at least one corrected route according toroute assessment criteria.) As described herein, AI and/or ML techniquescan be utilized in some embodiments to perform the analysis of the routeassessment criteria to generate a corrected route.

In block S510, the method 500 includes transmitting the corrected routeto the UAV. In many embodiments, the corrected route is transmitted tothe UAV in a manner that enables the UAV, utilizing its processor ormicrocontroller and memory, to activate its propulsion means in order tomaneuver according to the corrected route to reach the target location.By various embodiments of the method 500, a UAV can be automaticallydispatched to a target location even if the UAV unexpectedly deviatesfrom the originally provided preferred route.

FIG. 6 depicts an embodiment of a method 600 for monitoring the positionof a UAV in flight, generating and suggesting exploration maneuvers to auser, and executing upon the selected maneuvers. In many embodiments,machine-readable instructions stored on the navigation module memory cancause the navigation module, in communication with a user device, toperform the method 600 of FIG. 6 . As shown, an embodiment of thismethod 600 includes receiving flight location data of a UAV during atleast a portion of a flight of the UAV in block S602, matching thelocation data to a position on the 3D map in block S604, generating atleast one suggested exploration route based on exploration criteria androute constraint criteria in block S606, optionally assigning to atleast one suggested exploration route a risk evaluation score accordingto exploration risk assessment criteria in optional block S608,displaying the at least one suggested exploration route on a display ofa user device in block S610, receiving a selected exploration route fromuser input in block S612, and transmitting the selected explorationroute to the UAV in block S614. In still further embodiments, AI and/orML techniques can be utilized to perform various portions of the method600. The blocks of method 600 and subsequent methods described hereinpresent an organization of the analyses required and is not intended tobe limited by any specific computational methodology.

In block S602, the method 600 includes receiving flight location data ofa UAV during at least a portion of a flight of the UAV. In variousembodiments, the flight of the UAV can include, but is not limited to, aportion of a movement along a preferred or corrected route to reach atarget location, a portion of a patrolling maneuver between multipletarget locations, a portion of a movement along a selected explorationroute as described herein, and a hovering or holding maneuver at atarget location. In some embodiments, global coordinate tracking and/orsimilar technology, as appreciated by those of skill in the art, can beemployed to receive flight location data of the UAV's position during aflight. The flight location data can be cross-referenced or correlatedwith locations on the 3D map in many embodiments. In some embodiments,flight location data can be received only at regular intervals (e.g., 30seconds, 1 minute, 2 minutes, 5 minutes, 7 minutes, 10 minutes, etc.).In other embodiments, flight location data is received as frequently andcontinuously as the employed tracking technology allows. In still otherembodiments, flight location data is received on demand, for example,after user input requesting updated flight location data.

In block S604, the method 600 includes identifying the target locationwith respect to the 3D map. In certain embodiments, this can includecorrelating a GPS coordinate or other indicator with a location on the3D map.

In block S606, the method 600 includes generating at least one suggestedexploration route based on exploration criteria. In many embodiments,the exploration criteria comprise at least one of a predicted scoutingsensor detection improvement, a collision safety buffer, a total routedistance or time, a maximum altitude, or a combination thereof. Byanalyzing at least one of these criteria, the method 600 canprocedurally identify and generate possible maneuvers to nearby,accessible, and/or beneficial alternate or additional target locationsthat can assist a user of the system.

A predicted scouting sensor detection improvement, as an explorationcriterion, seeks to improve the reliability or accuracy of measurementstaken by one or more scouting sensors equipped on the UAV, in manyembodiments. For example, a certain scouting sensor (e.g., a toxic gassensor) may only take reliable measurements when within a certaindistance of a feature of interest (e.g., a wildfire). Using a secondscouting sensor, the system can detect that the UAV is not within thenecessary distance of the feature of interest. Using a predictedscouting sensor detection improvement as an exploration criterion, themethod 600 can generate at least one suggested exploration route thatwould maneuver the UAV closer to the feature of interest. In alternativeembodiments, a suggested exploration route could maneuver the UAVfarther away from or change its orientation to the feature of interestto take a more accurate measurement with a different scouting sensor.

A collision safety buffer, as an exploration criterion, seeks tomaximize a UAV's proximity to the features of interest near the targetlocation up to a predetermined safe distance in many embodiments. Forexample, the original target location can position the UAV substantiallybeyond a predetermined collision safety buffer to the nearest obstacle(e.g., at about 20 m when the safety buffer is set to about 5 m.) Uponarrival, the UAV's position may prove to be too distant to be of optimalassistance. In these and similar embodiments, the method 600 cangenerate at least one exploration route that maneuvers the UAV closer tothe limit of its collision safety buffer.

A total route distance or time, as an exploration criterion, seeks toreposition the UAV within a certain maximum distance or flight time thatcan offer alternative or superior vantage than the original targetlocation in many embodiments. The term “total route distance” and “totalroute time” are intended to mean the distance or time required tomaneuver the UAV from a starting position to the end point of thesuggested exploration route. In certain embodiments, however, asuggested exploration route can be designed to loop back to its startingposition. In these scenarios, the whole loop of the suggestedexploration route can be considered the total route distance or time. Insome embodiments, the total route distance, as an exploration criterion,can be from about 1 m to about 10 km. In some further embodiments, thetotal route distance can be from about 1 m to about 1000 m. In otherembodiments, the total route distance can be from about 1 m to about 100m. In still other embodiments, the total route distance can be fromabout 1 m to about 50 m. In further embodiments, the total routedistance can be from about 1 m to about 25 m. In still furtherembodiments, the total route distance can be from about 1 m to about 10m. In additional embodiments, the total route distance can be from about100 m to about 250 m. In some embodiments, the total route time can befrom about 1 second to about 12 hours. In some further embodiments, thetotal route time can be from about 1 second to about 1 hour. In otherembodiments, the total route time can be from about 1 second to about 15minutes. In still other embodiments, the total route time can be fromabout 1 second to about 7 minutes. In further embodiments, the totalroute time can be from about 30 seconds to about 5 minutes. In stillfurther embodiments, the total route time can be from about 5 minutes toabout 30 minutes. In many embodiments, a total route distance or time,as an exploration criterion, is determined according to the knowncapacities of the UAV being deployed. In various embodiments, the method600 can generate at least one suggested exploration route that relocatesthe UAV to a position within a predetermined total route distance ortime.

A maximum altitude, as an exploration criterion, seeks to elevate theUAV to a predetermined maximum altitude to achieve a potentiallysuperior vantage over the target location, in many embodiments. In somesituations, the original target location can be at an altitude lowerthan a maximally allowed altitude, and a better view might be achievableat a higher position. In many embodiments, a maximum altitude, as anexploration criterion, is determined according to the known capacitiesof the UAV being deployed. In these embodiments, the method 600 cangenerate at least one suggested exploration route having a higheraltitude than the original target location but beneath a maximumaltitude limit.

In order to avoid navigating the UAV into a known obstacle (e.g., atree, a building, a hillside, etc.), the at least one suggestedexploration route can be additionally generated according to the 3D mapdata and considering route constraint criteria, as discussed above inFIG. 3 in many embodiments. In certain embodiments, the route constraintcriteria can include, but are not limited to, at least one of acollision safety buffer, a total route distance or time, a maximumaltitude, at least one geofenced no-fly zone, a remaining battery lifeof the UAV, or a combination thereof.

In optional block S608, the method 600 can include assigning to at leastone suggested exploration route a risk evaluation score according toexploration risk assessment criteria. In many embodiments, theexploration risk assessment criteria can include but are not limited toat least one of a minimum altitude change, a maximum altitude, aduration of travel time spent above a predetermined altitude threshold,collision risk indicators, weather risk indicators, environment riskindicators, or a combination thereof. In many embodiments, theseexploration risk assessment criteria operate at least similarly to thoseof the route assessment criteria as discussed above for the method 400of FIG. 4 . As described herein, AI and/or ML techniques can be utilizedin some embodiments to perform the analysis of the exploration riskassessment criteria to generate one or more suggested explorationroutes. In some embodiments, the method 600 deletes or otherwise removesfrom consideration any suggested exploration routes that fail to scoresufficiently favorably to meet a predetermined risk threshold.Furthermore, in some embodiments, the method 600 can additionallycompare a plurality of suggested exploration routes and eliminate thosewhich surpass a predetermined route similarity threshold to a betterrated route. This prevents the consideration and subsequent presentationof a long list of nearly identical suggested exploration routes.

In block S610, the method 600 includes displaying the at least onesuggested exploration route on a display of a user device that is incommunication with the system. This notifies a user of what options areavailable for exploration routes. In some embodiments, the method 600also displays each suggested exploration route along with itscorresponding risk evaluation score if available. This can inform theuser of which options pose a greater risk to the UAV than others. Insome embodiments, the method 600 displays all generated suggestedexploration routes. In other embodiments, the method 600 only displaysthose that surpassed a predetermined risk threshold as described above.In still further embodiments, the method 600 displays only a subset ofthe generated suggested exploratory routes.

In block S612, the method 600 includes receiving a selected explorationroute from user input. In many embodiments, a user, interacting with agraphical user interface (GUI) or a user input element (e.g., button,slider, etc.) on a user device in communication the system, can inputwhich of the at least one suggested exploration routes he or she wouldlike the UAV to perform.

In block S614, the method 600 includes transmitting the selectedexploration route to the UAV. In many embodiments, the corrected routeis transmitted to the UAV in a manner that enables the UAV, utilizingits processor or microcontroller and memory, to activate its propulsionmeans in order to maneuver according to the selected exploration route.By various embodiments of the method 600, a UAV can be semi-autonomouslydispatched along exploration routes procedurally generated by the systemand/or method 600.

The methods described herein present their blocks in a particular orderfor ease of description only and should not have their sequencenecessarily interpreted as limiting. One of skill in the art willappreciate that, in many embodiments, the methods herein can beperformed in various sequences.

The systems and methods of the preferred embodiment and variationsthereof can be embodied and/or implemented at least in part as a machineconfigured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are preferably executedby computer-executable components preferably integrated with the systemand one or more portions of the processor on the UAV and/or computingdevice. The computer-readable medium can be stored on any suitablecomputer-readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (e.g., CD or DVD), hard drives, floppy drives, or anysuitable device. The computer-executable component is preferably ageneral or application-specific processor, but any suitable dedicatedhardware or hardware/firmware combination can alternatively oradditionally execute the instructions.

As used in the description and claims, the singular form “a”, “an” and“the” include both singular and plural references unless the contextclearly dictates otherwise. For example, the term “UAV” may include, andis contemplated to include a plurality of UAVs. At times, the claims anddisclosure may include terms such as “a plurality,” “one or more,” or“at least one;” however, the absence of such terms is not intended tomean, and should not be interpreted to mean, that a plurality is notconceived.

The term “about” or “approximately,” when used before a numericaldesignation or range (e.g., to define a length or pressure), indicatesapproximations which may vary by (+) or (−) 5%, 1% or 0.1%. Allnumerical ranges provided herein are inclusive of the stated start andend numbers. The term “substantially” indicates mostly (i.e., greaterthan 50%) or essentially all of a device, substance, or composition.

As used herein, the term “comprising” or “comprises” is intended to meanthat the devices, systems, and methods include the recited elements, andmay additionally include any other elements. “Consisting essentially of”shall mean that the devices, systems, and methods include the recitedelements and exclude other elements of essential significance to thecombination for the stated purpose. Thus, a system or method consistingessentially of the elements as defined herein would not exclude othermaterials, features, or steps that do not materially affect the basicand novel characteristic(s) of the claimed disclosure. “Consisting of”shall mean that the devices, systems, and methods include the recitedelements and exclude anything more than a trivial or inconsequentialelement or step. Embodiments defined by each of these transitional termsare within the scope of this disclosure.

The examples and illustrations included herein show, by way ofillustration and not of limitation, specific embodiments in which thesubject matter may be practiced. Other embodiments may be utilized andderived therefrom, such that structural and logical substitutions andchanges may be made without departing from the scope of this disclosure.Such embodiments of the inventive subject matter may be referred toherein individually or collectively by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any single invention or inventive concept, if more thanone is in fact disclosed. Thus, although specific embodiments have beenillustrated and described herein, any arrangement calculated to achievethe same purpose may be substituted for the specific embodiments shown.This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,will be apparent to those of skill in the art upon reviewing the abovedescription.

1-51. (canceled)
 52. A computer-implemented method for dispatching andnavigating an unmanned aerial vehicle (UAV) to a target location, themethod comprising: accessing a 3D map comprising LiDAR map data orphotogrammetric calculations aligned to global coordinates; identifyinga location of the UAV with respect to the 3D map via the globalcoordinates; receiving an input comprising a target location;determining the target location with respect to the 3D map; generatingat least one suggested exploration route between the location of the UAVto the target location, wherein the at least one suggested explorationroute is based on at least one exploration criterion comprising at leastone of: a predicted scouting sensor detection improvement, a collisionsafety buffer, a total route distance or time, a maximum altitude, or acombination thereof; assigning to the at least one suggested explorationroute, a risk evaluation score according to at least one explorationrisk assessment criterion comprising at least one of: a minimum altitudechange, a maximum altitude, a duration of travel time spent above apredetermined altitude threshold, collision risk indicators, weatherrisk indicators, environment risk indicators, or a combination thereof;determining whether the at least one suggested exploration route meetsor exceeds a predetermined risk evaluation score threshold; and when theat least one suggested exploration route meets or exceeds thepredetermined risk evaluation score threshold, cause automatic dispatchof the UAV to the target location via the at least one suggestedexploration route.
 53. The computer-implemented method of claim 52,further comprising receiving sensor data from at least one scoutingsensor coupled to the UAV; and transmitting at least a portion of thesensor data to a user device.
 54. The computer-implemented method ofclaim 53, wherein the at least one scouting sensor is selected from thegroup consisting of a camera, an infrared camera, an image sensor, amicrophone, an acoustic sensor, a LiDAR sensor, an ultrasonic sensor, asonar sensor, a radar sensor, a gyroscope sensor, an electrochemicaltoxic gas sensor, a temperature sensor, a humidity sensor, a proximitysensor, a barometric air pressure sensor, a radiation sensor, or acombination thereof.
 55. The computer-implemented method of claim 52,wherein the method is performed by a navigation module.
 56. Thecomputer-implemented method of claim 55, wherein the navigation moduleis physically attached to the UAV.
 57. The computer-implemented methodof claim 55, wherein the navigation module is electronically integratedinto and in electrical communication with the UAV.
 58. Thecomputer-implemented method of claim 55, wherein the navigation moduleis one or more computing devices on a cloud network system.
 59. Thecomputer-implemented method of claim 55, wherein the navigation moduleis a virtual machine.
 60. The computer-implemented method of claim 55,wherein the navigation module is a user device communicatively coupledto the UAV.
 61. The computer-implemented method of claim 55, furthercomprising receiving at least one of: updated 3D map data, updatedgeofenced no-fly zones, updated drop-off or landing zones, updatedcollision risk indicators, updated weather risk indicators, or updatedenvironment risk indicators; and updating, in the 3D map, one or morezone indicator tags based on the at least one of: the updated 3D mapdata, the updated geofenced no-fly zones, the updated drop-off orlanding zones, the updated collision risk indicators, the updatedweather risk indicators, and the updated environment risk indicators.62. The computer-implemented method of claim 55, further comprisingreceiving the input from a computer-aided dispatch (CAD) system incommunication with the navigation module.
 63. A system for dispatchingand navigating an unmanned aerial vehicle (UAV) to a target locationcomprising: a navigation module in communication with a UAV, thenavigation module comprising: a processor; and a memory storing a 3D mapcomprising the target location and machine-readable instructions suchthat, when executed by the processor, cause the processor to perform amethod comprising: determining a location of the UAV with respect to the3D map, wherein the 3D map comprises LiDAR map data or photogrammetriccalculations aligned to global coordinates; receiving an inputindicating a target location; determining the target location withrespect to the 3D map; generating at least one suggested explorationroute between the location of the UAV to the target location, whereinthe at least one suggested exploration route is based on at least oneexploration criterion comprising at least one of: a predicted scoutingsensor detection improvement, a collision safety buffer, a total routedistance or time, a maximum altitude, or a combination thereof;assigning to the at least one suggested exploration route a riskevaluation score according to at least one exploration risk assessmentcriterion comprising at least one of: a minimum altitude change, amaximum altitude, a duration of travel time spent above a predeterminedaltitude threshold, collision risk indicators, weather risk indicators,environment risk indicators, or a combination thereof; determiningwhether the at least one suggested exploration route meets or exceeds apredetermined risk evaluation score threshold; and when the at least onesuggested exploration route meets or exceeds the predetermined riskevaluation score threshold, cause automatic dispatch of the UAV to thetarget location via the at least one suggested exploration route. 64.The system of claim 63, further comprising the UAV and a user device incommunication with the navigation module and the UAV.
 65. The system ofclaim 64, wherein the user device transmits the input to the navigationmodule after receiving a user input at the user device.
 66. The systemof claim 63, further comprising the UAV, wherein the UAV comprises atleast one scouting sensor, wherein the machine-readable instructionsstored on the memory further instruct the processor to: cause the UAV toobtain sensor data from the at least one scouting sensor; and cause theUAV to transmit the obtained sensor data to a user device.
 67. Thesystem of claim 66, wherein the at least one scouting sensor is selectedfrom the group consisting of a camera, an infrared camera, an imagesensor, a microphone, an acoustic sensor, a LiDAR sensor, an ultrasonicsensor, a sonar sensor, a radar sensor, a gyroscope sensor, anelectrochemical toxic gas sensor, a temperature sensor, a humiditysensor, a proximity sensor, a barometric air pressure sensor, aradiation sensor, or a combination thereof.
 68. The system of claim 63,further comprising the UAV, wherein the navigation module is physicallyattached to the UAV.
 69. The system of claim 63, further comprising theUAV, wherein the navigation module is electronically integrated into andin electrical communication with the UAV.
 70. The system of claim 63,wherein the navigation module is one or more computing devices on acloud network system.
 71. The system of claim 63, wherein themachine-readable instructions stored on the navigation module furtherinstruct the processor to: receive at least one of: updated 3D map data,updated geofenced no-fly zones, updated drop-off or landing zones,updated collision risk indicators, updated weather risk indicators, orupdated environment risk indicators; and updating, in the 3D map, one ormore zone indicator tags, based on the at least one of: the updated 3Dmap data, the updated geofenced no-fly zones, the updated drop-off orlanding zones, the updated collision risk indicators, the updatedweather risk indicators, and the updated environment risk indicators.